Data overview

Participants

The current study is based on a sample 39 healthy adult participants (X females; age XX ± XX, range X-X , XX). The current study sample was obtained from an initial pool of 44 participants from which 3 participants were excluded due to excessive motion in the scanner and 1 was excluded due to technical problems during scanning.

MRI data acquisition

MRI data were recorded on a Philips Achieva 3 Tesla scanner (Best, The Netherlands) using a 32-element receive head coil. Using a T2-weighted whole-brain gradient-echo planar image sequence, 440 volumes were acquired for each experimental block [Slices = XXX; repetition time = XX s; echo time = XXX ms; slice gap = XXX mm; voxel size = 3 x 3 x 3.5 mm3; flip angle = XXX°; field of view = 240 x 127.5 x 240 mm2; SENSE-factor = 2]. In addition, a field map and a high-resolution T1-weighted anatomical image were acquired. fMRI data preprocessing and analysis were performed in the SPM12 toolbox. Preprocessing included b0 fieldmap correction, slice time correction and corregistration of the functional data to the T1-weighted image. The deformation fields derived from segmentation of the T1 image were used for normalization to the Montreal Neurological Institute (MNI) -152 template space. Last, smoothing with a 6 mm full-width-half-maximum kernel was applied to the functional data.

MR quality assessment

Motion artifacts were assessed by calculating the framewise displacement (FD) values of each subject and task recording. Only subjects with FD < 0.5 mm were included in analysis (mean XX±XX; no excluded participants based on this criteria). Moreover, single volumes with FD > 1 were censored in the statistical analyses using an additional binary regressor (mean XX±XX % of volumes excluded; and a maximum of XX % excluded in one participant).

# Data read, trim, reshape
df <- readxl::read_xlsx('O:/studies/grapholemo/LEMO_Master.xlsx',sheet = 'MRI')
df <- dplyr::select(df,c(subjID,grep('FWD_*symCtrl*',colnames(df)) ))
colnames(df) <-gsub('FWD_','',colnames(df))
dflong <- tidyr::pivot_longer(df,colnames(df)[-1]) %>% separate(name,c('task','measure'),sep="__")
dflong$measure <- as.factor(dflong$measure)
levels(dflong$measure) <- c('max','mean','n volumes with FD > 1')

#Exclude subjects rows with no MRI data
dflong<-dflong[!is.na(dflong$value),]

#exclude subjects based on mean FD 
`%nin%` = Negate(`%in%`)
excludedSubjects <-dflong$subjID[which(dflong$value > 0.5 & dflong$measure == 'mean')]
dflong<-dflong[which(dflong$subjID %nin% excludedSubjects),]

#exclude subjects based on % of 'bad scans'
#excludedSubjects2 <-dflong$subjID[which(dflong$ > 0.5 & dflong$measure == 'mean')]

#Plot 
  ggplot(dflong,aes(x=task,y=value,fill=measure))+
  geom_point(shape=21,alpha=.5,color='black',size=2)+
  facet_wrap(~measure,scales='free')+
  theme_bw() + 
  scale_fill_discrete(guide="none")+
  scale_x_discrete(limits = rev)+
  labs(title=paste0('Framewise displacement (N=',length(unique(dflong$subjID)),')'),
                    subtitle = paste0(length(excludedSubjects), ' subject(s) was excluded based on mean FD'))

Results

fMRI: whole-brain

  • Results displayed based on p uncorrected after cluster extension correction.
  • Conditions are: L(letters), FFF(familiar falsefonts), FFL(learned falsefonts),FFN (new falsefonts),. (baseline)
  • FFF are presented identical at pre and post, FFL were presented also in learning task, FFN were different at pre and post

Pretest

Results table
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel/SymbolControl/symCtrl_pre/Result_regions_2Lv_GLM0.xlsx')
#some formatting 
dat$file <- substr(dat$file,unlist(gregexpr('con_0',dat$file)),unlist(gregexpr('con_0',dat$file))+7) #keep just the con_**** as name 
dat <- dplyr::relocate(dat,'aal',2) 
    dat <-  dplyr::relocate(dat,'file',1)
    cons2plot <- unique(dat$file)  


# Rename contrasts 
urfile <- dat$file 
renamecontrasts <- function(originalNames){
    
    originalNames <- gsub('con_0001','L>FFL',originalNames)
    originalNames <- gsub('con_0002','L>FFF',originalNames)
    originalNames <- gsub('con_0003','L>FFN',originalNames)
    originalNames <- gsub('con_0004','FFL>L',originalNames)
    originalNames <- gsub('con_0005','FFL>FFF',originalNames)
    originalNames <- gsub('con_0006','FFL>FFN',originalNames)
    originalNames <- gsub('con_0007','FFF>L',originalNames)
    originalNames <- gsub('con_0008','FFN>L',originalNames)
    originalNames <- gsub('con_0009','L>.',originalNames)
    originalNames <- gsub('con_0010','FFL>.',originalNames)
    originalNames <- gsub('con_0011','FFF>.',originalNames)
    originalNames <- gsub('con_0012','FFN>.',originalNames)  
  
}

dat$file <- renamecontrasts(dat$file) 

knitr::kable(dat,caption='', digits=3) %>% 
    #kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
    column_spec(1, bold = T, border_right = T ) %>%
    kable_classic(full_width = F, html_font = "Calibri")
file aal xcoord ycoord zcoord cluster_pFWE cluster_punc cluster_k peak_Z peak_T peak_pFWE peak_punc T_heightThresh dist
L>FFL Cuneus_L -8 -93 18 0.000 0 171 4.88 5.81 0.040 0 3.319 0
L>FFL Temporal_Mid_L -50 -48 15 0.000 0 377 4.31 4.93 0.327 0 3.319 0
L>FFL Temporal_Mid_R 70 -54 9 0.000 0 247 4.05 4.56 0.632 0 3.319 4
L>FFF Temporal_Mid_L -53 -45 -6 0.000 0 164 4.1 4.63 0.541 0 3.327 0
L>FFN Occipital_Inf_L -17 -93 -9 0.000 0 192 5.62 7.08 0.001 0 3.324 0
L>FFN Cuneus_R 19 -96 9 0.000 0 201 5.15 6.25 0.010 0 3.324 0
L>FFN Precentral_L -62 12 33 0.006 4e-04 89 4 4.50 0.689 0 3.324 1
FFL>L Occipital_Mid_L -35 -93 3 0.000 0 844 6.31 8.46 0.000 0 3.443 0
FFL>L Occipital_Mid_R 37 -87 15 0.000 0 914 6.11 8.04 0.000 0 3.443 0
FFL>FFF Lingual_L -20 -84 -12 0.001 1e-04 145 5.95 7.73 0.000 0 3.38 0
FFL>FFF Calcarine_R 19 -96 -3 0.005 4e-04 105 4.49 5.20 0.159 0 3.38 0
FFL>FFN Calcarine_R 22 -93 -3 0.000 0 622 7.48 11.43 0.000 0 3.35 0
FFL>FFN Occipital_Mid_L -23 -96 6 0.000 0 700 6.92 9.90 0.000 0 3.35 0
FFF>L Occipital_Mid_R 34 -81 3 0.000 0 929 6.43 8.73 0.000 0 3.482 0
FFF>L Occipital_Mid_L -44 -87 3 0.000 0 549 5.93 7.67 0.000 0 3.482 0
FFN>L Occipital_Mid_L -47 -78 0 0.000 0 363 6.69 9.34 0.000 0 3.388 0
FFN>L Occipital_Inf_R 43 -75 0 0.000 0 656 6 7.81 0.000 0 3.388 0
L>. Occipital_Inf_R 34 -90 0 0.000 0 4479 Inf 23.42 0.000 0 3.325 0
L>. Parietal_Inf_R 28 -54 51 0.000 0 284 6.11 8.04 0.000 0 3.325 0
L>. Precentral_R 55 3 51 0.000 0 351 5.65 7.14 0.001 0 3.325 0
L>. Parietal_Inf_L -29 -51 48 0.000 0 289 5.59 7.03 0.001 0 3.325 0
L>. Precentral_L -47 3 39 0.000 0 483 5.53 6.92 0.001 0 3.325 0
L>. Supp_Motor_Area_R 4 6 60 0.000 0 392 5.43 6.73 0.002 0 3.325 0
L>. Frontal_Mid_L -41 33 45 0.008 5e-04 80 4.33 4.96 0.331 0 3.325 0
L>. Frontal_Sup_Medial_R 7 60 42 0.039 0.0024 56 4.29 4.90 0.371 0 3.325 1
L>. Frontal_Inf_Orb_L -41 48 -15 0.011 6e-04 75 4.23 4.82 0.438 0 3.325 0
FFL>. Occipital_Inf_R 34 -90 0 0.000 0 2173 Inf 20.44 0.000 0 3.325 0
FFL>. Occipital_Inf_L -38 -84 -9 0.000 0 2040 Inf 19.38 0.000 0 3.325 0
FFL>. Precentral_R 55 3 51 0.000 0 179 5.77 7.37 0.000 0 3.325 0
FFL>. Caudate_L -5 21 6 0.003 1e-04 88 5.54 6.93 0.001 0 3.325 1.41
FFL>. Angular_R 28 -57 54 0.001 0 106 5.24 6.41 0.006 0 3.325 0
FFL>. Precentral_L -56 -3 48 0.000 0 293 5.2 6.33 0.008 0 3.325 0
FFL>. Hippocampus_L -17 -33 0 0.010 6e-04 69 5.12 6.20 0.012 0 3.325 0
FFL>. Parietal_Inf_L -29 -51 48 0.000 0 127 4.74 5.58 0.082 0 3.325 0
FFL>. Occipital_Mid_R 28 -69 30 0.022 0.0012 59 4.73 5.56 0.088 0 3.325 0
FFL>. Thalamus_L -14 -30 24 0.000 0 178 4.65 5.44 0.122 0 3.325 7.81
FFL>. Supp_Motor_Area_L -8 9 51 0.001 0 109 4.51 5.23 0.202 0 3.325 0
FFL>. Frontal_Inf_Tri_L -56 33 21 0.011 6e-04 68 4.22 4.80 0.491 0 3.325 0
FFF>. Occipital_Inf_R 34 -90 0 0.000 0 2204 Inf 21.32 0.000 0 3.319 0
FFF>. Occipital_Mid_L -29 -93 0 0.000 0 2017 Inf 18.45 0.000 0 3.319 0
FFF>. Parietal_Sup_R 28 -57 57 0.000 0 187 5.59 7.03 0.001 0 3.319 0
FFF>. Parietal_Inf_L -29 -45 45 0.000 0 180 5.5 6.86 0.002 0 3.319 0
FFF>. Precentral_R 55 3 48 0.003 2e-04 109 5.27 6.45 0.005 0 3.319 0
FFF>. Frontal_Mid_L -44 27 48 0.014 0.001 79 5.23 6.38 0.007 0 3.319 0
FFF>. Caudate_L -14 24 9 0.000 0 178 5.07 6.12 0.015 0 3.319 0
FFF>. Precentral_L -56 -3 51 0.000 0 181 5.07 6.12 0.015 0 3.319 1
FFF>. Occipital_Mid_R 28 -75 33 0.033 0.0023 65 4.89 5.82 0.037 0 3.319 0
FFF>. Hippocampus_L -26 -39 15 0.031 0.0021 66 4.67 5.47 0.090 0 3.319 5.48
FFF>. Frontal_Mid_Orb_R 37 66 -9 0.015 0.001 78 4.22 4.80 0.408 0 3.319 2
FFN>. Fusiform_R 37 -66 -15 0.000 0 2145 Inf 19.61 0.000 0 3.322 0
FFN>. Occipital_Inf_L -35 -84 -6 0.000 0 2175 Inf 17.68 0.000 0 3.322 0
FFN>. Angular_R 28 -57 54 0.000 0 375 6.09 8.00 0.000 0 3.322 0
FFN>. Precentral_R 52 3 51 0.000 0 244 5.92 7.67 0.000 0 3.322 0
FFN>. Postcentral_L -56 -6 51 0.000 0 299 5.64 7.11 0.001 0 3.322 1
FFN>. Cingulum_Post_R 13 -36 21 0.010 6e-04 74 4.92 5.86 0.034 0 3.322 4.24
FFN>. Supp_Motor_Area_L -5 -3 63 0.000 0 133 4.61 5.38 0.133 0 3.322 0
FFN>. Precuneus_L -23 -42 21 0.008 4e-04 78 4.59 5.35 0.142 0 3.322 9.9
Contrasts
L>FFL

L>FFF

L>FFN

FFL>L

FFL>FFF

FFL>FFN

FFF>L

FFN>L

L>.

FFL>.

FFF>.

FFN>.

Posttest

Results table
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel/SymbolControl/symCtrl_post/Result_regions_2Lv_GLM0.xlsx')
#some formatting 
dat$file <- substr(dat$file,11,18)
dat <- relocate(dat,'aal',2) 
dat <-  relocate(dat,'file',1)
cons2plot <- unique(dat$file)
# Rename contrasts 
urfile <- dat$file 
dat$file <- renamecontrasts(dat$file)

knitr::kable(dat,caption='', digits=3) %>% 
    #kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
    column_spec(1, bold = T, border_right = T ) %>%
    kable_classic(full_width = F, html_font = "Calibri")
file aal xcoord ycoord zcoord cluster_pFWE cluster_punc cluster_k peak_Z peak_T peak_pFWE peak_punc T_heightThresh dist
L>FFL Cuneus_R 10 -81 27 0.000 0 220 4.3 4.91 0.316 0 3.324 0
L>FFL Precuneus_R 10 -45 54 0.002 1e-04 123 4.2 4.77 0.420 0 3.324 0
L>FFL SupraMarginal_L -56 -27 21 0.000 0 221 4.17 4.73 0.452 0 3.324 0
L>FFL SupraMarginal_R 58 -24 27 0.002 1e-04 121 4.15 4.71 0.470 0 3.324 0
L>FFL Temporal_Mid_R 49 -60 12 0.000 0 181 4.15 4.70 0.476 0 3.324 0
L>FFL Rolandic_Oper_R 55 6 6 0.022 0.0016 73 4 4.49 0.658 0 3.324 0
L>FFF Precentral_L -53 6 36 0.000 0 216 4.04 4.54 0.597 0 3.42 0
L>FFN Calcarine_R 16 -96 6 0.018 0.0013 76 4.83 5.73 0.045 0 3.343 0
FFL>L Occipital_Mid_R 34 -90 9 0.000 0 908 6.74 9.46 0.000 0 3.337 0
FFL>L Occipital_Mid_L -32 -87 0 0.000 0 896 6.11 8.03 0.000 0 3.337 0
FFL>L Cingulum_Mid_R 10 -27 27 0.000 0 175 4.78 5.65 0.055 0 3.337 4.9
FFL>L Occipital_Sup_R 28 -63 33 0.000 0 215 4.74 5.59 0.064 0 3.337 0
FFL>FFF Occipital_Inf_R 22 -90 -3 0.000 0 246 7.04 10.22 0.000 0 3.337 0
FFL>FFF Calcarine_L -11 -93 -12 0.000 0 297 5.62 7.08 0.001 0 3.337 0
FFL>FFF Occipital_Sup_R 22 -63 45 0.000 0 1227 5.08 6.13 0.014 0 3.337 0
FFL>FFF Frontal_Inf_Oper_R 40 12 27 0.000 0 170 4.79 5.66 0.051 0 3.337 0
FFL>FFF Insula_L -29 27 -3 0.004 3e-04 108 4.72 5.55 0.067 0 3.337 0
FFL>FFF Cingulum_Post_L 1 -33 27 0.002 1e-04 126 4.63 5.41 0.095 0 3.337 2.24
FFL>FFF Frontal_Inf_Oper_L -44 12 21 0.000 0 273 4.51 5.22 0.151 0 3.337 0
FFL>FFF Supp_Motor_Area_L -2 18 45 0.001 1e-04 142 4.2 4.78 0.395 0 3.337 0
FFL>FFN Occipital_Inf_L -17 -93 -9 0.000 0 451 6.67 9.29 0.000 0 3.323 0
FFL>FFN Calcarine_R 19 -96 0 0.000 0 329 6.32 8.50 0.000 0 3.323 0
FFL>FFN Parietal_Inf_L -32 -57 45 0.000 0 390 4.56 5.30 0.137 0 3.323 0
FFL>FFN Precuneus_L -5 -63 42 0.000 0 524 4.52 5.25 0.155 0 3.323 0
FFL>FFN Cingulum_Post_L 1 -36 27 0.000 0 181 4.46 5.15 0.195 0 3.323 1.41
FFL>FFN Frontal_Mid_L -50 24 42 0.000 0 249 4.37 5.01 0.264 0 3.323 0
FFL>FFN Cingulum_Mid_R 1 21 42 0.001 0 137 4.22 4.80 0.408 0 3.323 0
FFL>FFN Frontal_Mid_L -38 57 12 0.048 0.0033 58 4.06 4.58 0.593 0 3.323 0
FFF>L Occipital_Mid_L -50 -84 6 0.000 0 585 6.41 8.70 0.000 0 3.323 2.83
FFF>L Occipital_Mid_R 40 -90 3 0.000 0 718 6.15 8.13 0.000 0 3.323 0
FFN>L Occipital_Mid_R 34 -81 6 0.000 0 1410 6.84 9.70 0.000 0 3.328 0
FFN>L Occipital_Mid_L -50 -84 3 0.000 0 906 6.08 7.99 0.000 0 3.328 2.83
L>. Occipital_Mid_R 34 -93 3 0.000 0 1707 Inf 15.78 0.000 0 3.336 0
L>. Occipital_Inf_L -35 -84 -6 0.000 0 1513 Inf 15.11 0.000 0 3.336 0
L>. Precentral_L -47 0 54 0.000 0 183 4.58 5.33 0.110 0 3.336 0
L>. Parietal_Sup_L -32 -63 57 0.000 0 214 4.58 5.33 0.111 0 3.336 0
L>. Supp_Motor_Area_L -5 6 54 0.016 0.0013 85 4.33 4.96 0.264 0 3.336 0
FFL>. Occipital_Inf_L -35 -87 -9 0.000 0 1776 Inf 17.81 0.000 0 3.328 0
FFL>. Occipital_Mid_R 31 -93 3 0.000 0 2010 Inf 17.08 0.000 0 3.328 0
FFL>. Parietal_Sup_L -32 -63 60 0.000 0 686 5.8 7.43 0.000 0 3.328 0
FFL>. Precentral_L -47 3 54 0.000 0 663 5.56 6.97 0.001 0 3.328 0
FFL>. Angular_R 31 -57 51 0.000 0 366 5.3 6.51 0.004 0 3.328 0
FFL>. Frontal_Mid_R 49 21 51 0.000 0 338 4.89 5.82 0.037 0 3.328 0
FFL>. Supp_Motor_Area_L -8 12 45 0.005 3e-04 96 4.26 4.87 0.358 0 3.328 0
FFL>. Cingulum_Mid_L -14 -33 27 0.017 0.0011 75 4.15 4.71 0.482 0 3.328 6.16
FFF>. Occipital_Inf_R 34 -90 0 0.000 0 1883 Inf 18.38 0.000 0 3.32 0
FFF>. Occipital_Mid_L -29 -93 3 0.000 0 1608 Inf 16.62 0.000 0 3.32 0
FFN>. Occipital_Inf_L -35 -84 -6 0.000 0 1750 Inf 17.07 0.000 0 3.341 0
FFN>. Temporal_Inf_R 49 -69 -9 0.000 0 2069 Inf 16.44 0.000 0 3.341 0
FFN>. Precentral_L -50 0 54 0.000 0 169 6.53 8.97 0.000 0 3.341 0
FFN>. Precentral_R 55 6 48 0.000 0 179 5.65 7.13 0.001 0 3.341 0
FFN>. Angular_R 28 -57 54 0.006 4e-04 83 5.16 6.26 0.010 0 3.341 0
FFN>. Occipital_Mid_R 31 -69 27 0.043 0.0026 54 4.98 5.96 0.025 0 3.341 0
FFN>. Parietal_Sup_L -29 -63 57 0.001 1e-04 116 4.59 5.36 0.134 0 3.341 0
Contrasts
L>FFL

L>FFF

L>FFN

FFL>L

FFL>FFF

FFL>FFN

FFF>L

FFN>L

L>.

FFL>.

FFF>.

FFN>.

Pre>Post

Results table
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel_pairedTs/symCtrl_prePost/Result_regions_2Lv_GLM0prePost.xlsx')
#some formatting 
dat$file <- substr(dat$file,11,18)
dat <- relocate(dat,'aal',2) 
dat <-  relocate(dat,'file',1)
cons2plot <- unique(dat$file)
# Rename contrasts 
urfile <- dat$file 
dat$file <- renamecontrasts(dat$file)

knitr::kable(dat,caption='', digits=3) %>% 
    #kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
    column_spec(1, bold = T, border_right = T ) %>%
    kable_classic(full_width = F, html_font = "Calibri")
file aal xcoord ycoord zcoord cluster_pFWE cluster_punc cluster_k peak_Z peak_T peak_pFWE peak_punc T_heightThresh dist
L>FFL Occipital_Mid_R 31 -63 33 0.000 0 274 4.75 5.60 0.062 0 3.32 0
FFF>. Calcarine_R 7 -78 6 0.000 0 301 4.27 4.87 0.377 0 3.327 0
FFF>. Hippocampus_L -26 -39 18 0.002 1e-04 109 4.06 4.58 0.621 0 3.327 8.31
Contrasts
L>FFL

FFF>.

Post>Pre

Results table
dat <- readxl::read_xlsx('O:/studies/grapholemo/analysis/LEMO_GFG/mri/2ndLevel_pairedTs/symCtrl_prePost/Result_regions_2Lv_GLM0postPre.xlsx')
#some formatting 
dat$file <- substr(dat$file,11,18)
dat <- relocate(dat,'aal',2) 
dat <-  relocate(dat,'file',1)
cons2plot <- unique(dat$file)
# Rename contrasts 
urfile <- dat$file 
dat$file <- renamecontrasts(dat$file)

knitr::kable(dat,caption='', digits=3) %>% 
    #kable_styling(bootstrap_options = c("striped", "hover"), full_width = F) %>%
    column_spec(1, bold = T, border_right = T ) %>%
    kable_classic(full_width = F, html_font = "Calibri")
file aal xcoord ycoord zcoord cluster_pFWE cluster_punc cluster_k peak_Z peak_T peak_pFWE peak_punc T_heightThresh dist
FFL>L Occipital_Mid_R 31 -63 33 0.000 0 274 4.75 5.60 0.062 0 3.32 0
FFL>FFF Occipital_Sup_R 25 -66 45 0.000 0 199 4.14 4.69 0.475 0 3.341 0
FFL>FFF Parietal_Inf_L -29 -51 42 0.000 0 333 4.13 4.67 0.489 0 3.341 0
FFL>FFN Parietal_Inf_L -29 -54 42 0.000 0 166 4.34 4.98 0.266 0 3.324 0
FFL>FFN Angular_R 28 -54 45 0.000 0 189 4.21 4.79 0.393 0 3.324 0
FFL>. Parietal_Inf_L -47 -51 45 0.003 2e-04 104 3.75 4.16 0.916 1e-04 3.334 0
FFN>. Occipital_Inf_R 37 -96 -6 0.000 0 155 4.66 5.46 0.097 0 3.33 1
FFN>. Occipital_Mid_L -29 -102 -6 0.005 3e-04 91 4.39 5.05 0.256 0 3.33 3.74
Contrasts
FFL>L

FFL>FFF

FFL>FFN

FFL>.

FFN>.